{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# pandas中的绘图函数\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pandas import DataFrame, Series\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 线型图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1daef414518>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JhhOny/jn2t1c270NXdo0tySDqp9bLm9PM3/duMZd2HuN4FnbaaKtVP8D/xCA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\naSFQSikP9/91hbcqQSCEUAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1daef522c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "s = Series(np.random.randn(10).cumsum(), index=np.arange(0, 100, 10))\n",
    "s.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1daef70aef0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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zvkLj5qZ2Wfkm/ncGUNOmphlAZqjfqNVys3cf9GmphG7ciHV5dbaFk8zjmx1/\nM3v7VXa904aKFSy7kFxBumvMMbPGGrgOVARsgTNArYcd06hRI6FLShIpa38UEa8OEhdr1hIXq1UX\nf7dtJ+JmfC6yT50SRoPBvMPS+fDr1V9F7WW1xZbrWwp0vEGrFcnLl4srLVqKi9Wqi8hhw0XOxYtm\nrrJkMGi1InrMWNMMmokTi8UMmoJK27hRXKpdR1zr8rTIjYws9PliP5kmLlarLm7v3WeG6iQ16Q1G\n0WL6DtF/8eEiuR5qzK658+ryNPAVoAG+E0JMe9jz63pUED96e4PRiE1wEK4dO+HSqRP2tWqqOtPC\nKIy8uPlFknOS2dRzEw7WBZsTb8zKIuWHlSR/9x3GjAxcunTG862RZabfVZ+SQvSbb5Fz8iSeY8bg\nMWRwsZlBU1DZx44R9eZbKBoNgfPnFfhdWua+/UQNGYL7gAH4TJpo5iqlorbzcjyDlh1nfv+GPF3H\n1+LXU2V2TUHUcXMTO6ZMwbVTJ+yqVStWAXAq4RQD/xjIiHojeL3+64U6lyEjg+TvviNlxQ8IrRa3\n7t2p8MYb2Aaot9+jpeVev07UsOHoExLwm/EZrp07q12S2eRev0HUsGF3vrcZuHbu9FjH61NSuN69\nO9bl3AlZ97Nqd9dK5vPasmOcjUnn4IS22GgsP8NOrdk1j82uShW8Ro3Cvnr1YhXwAA28GtClYhe+\nO/8dsZmxhTqXxtUVr9Gjqbx9G+UHDCBj82bCu3QhbspUdAkJZqq4+Mg6fJibfV/EmJ1N8PJlpSrg\nAexCKxLy41rsa9YkZvRokpcuzffMGyEEse9/gDEtHb9ZM2XAlwLRqdnsvJLAC40DiyTgC6r4Vqai\nMY3GoKDwxYkvzHI+aw8PvN+bQKVtWynXsyepP/1EeMdOxM+ciT411SzXUFvaL78SOXgI1l6ehPz4\nY6kddLYuX56gZd/j0qUzCTNnEffxZIRe/8jj0n76mcydO/EcOwb7atWKoFLJ0n48Zpo53rdpoMqV\nPJwM+fvwcfJhUJ1B/HnzT07EnzDbeW18fPCdMplKWzbj0rEDKd99T3j7DiR+MxdDZqbZrlOUhNFI\nwhdfEjtpEk5NmxKyenWp7o4CsLKzw3/2bDyGDiXtxx+JGj7iof9+uddvEP/ZZzi1aEH5gQOLsFLJ\nUnQGI2uPRfFUNS8C3B3VLuehZMg/wCu1XsHHyYcZR2dgMJp3A2jboCD8P/+c0N824tSiBUnz5hHe\nrj3JS5eW8S8kAAAgAElEQVRizMkx67UsyajVEjN2LMmLFlGuTx8CFy4oM4trKVZWeI15G5+pU8g6\ndIiIfv3Rxf63e0/k5XHr3XexsrPDd/p0eWd0KfHXxXgSb+cWm41BHkb+j3sAB2sHxjYay6WUS2y4\ntsEi17CrUoWAb74m5Oefsa9Th4SZs7jWsSMpq1Yh8vIsck1z0ScnE/nyK9z+cyte776Lz+SPUWzU\n3+qsqLn37k3gooXobt3iZp8XyDl/4R9fT5w7D+2FC/hMnYKNt9yIprRYfTQSPzd72lQr/v+mpXY9\neXMQQvDKn69wM+Mmv/f8HRdbF4teL/v4cRK++oqc4yew8fOj/CuvYO3jjZW9PYqdPVb2dij29ljZ\nmf5U7OzufM2uSFuIudeumWbQJCfjN/NzXDt0KLJrF1faq1eJGj4cQ2oa/rNn49L2KdNSDgNfptzz\nvfCdOlXtEiUzuZmURZtZuxnToSoj21Up0muXmCmUJSXkAS4mX6Tv730ZWHMg7zR5x+LXE0KQtf+A\naRG0CxcefcAdiq3tP14ArOztUOzsUeztsLKzf8TX7P75QvI/Lyj//0Jiekx76RIxY8ai2NsROH8+\nDnXqWPCnUbLoEhKIHvE62kuX8Hx7NKlr1qDY2BD666/39jGQSr7pWy6xZP8NDk5oi7erfZFeu8xv\nGmIJNT1q8lyV51h1aRW9qvaioptlb2hSFAXnJ1rh1KolushIjNnZGLVaRG4uIjcXozYXkas1PabN\nxZhr+tP02P/8qdXe+5oh8zYiKenOY7n/+JMCvMjbVa1K4IJvsfHzs8BPoOSy8fIi+IcVxLw7jsTZ\nX4BGQ8ia1TLgS5FcvYGfjkfRoYZ3kQd8QcmQz4c3G7zJ1ptbmXV8FvPazSuSayqKYvGt8IQQCJ3O\nFPp3X0i0/3qx+NcLimJlheszz8jtEB/AytGRgK/nkLx4CdY+3jjULT57fUqF9+f5OFKzdfRvXvwH\nXO+SIZ8PFRwqMLzecGYdn8W+6H08EfCE2iWZhaIoppUVbW3LzKyYoqBoNFQYPkztMiQLWHU4kmAP\nR1pWevC+E8WNnF2TT/2q9yPYNZjPj32OzqhTuxxJkorY3/G3OXozhX5Ng7CyKl536j+MDPl8stHY\nMK7JOG5m3GTt5bVqlyNJUhFbdSQSW40VzzcKULuUxyJD/jE84f8ELf1b8u3pb0nRpqhdjiRJRSQn\nz8AvJ6PpXNsHD+eSte6QDPnHoCgK4xqPI0efw7xTRTMAK0mS+jadvcVtrb5E3OH6bzLkH1NouVD6\nVu/Lur/XcSXlitrlSJJUBFYdiaSylzNNK5a8XbxkyBfA8HrDcbV1ZcaxGXLzbkkq5c7HpHMmKo3+\nzYKK3dLo+SFDvgDc7Nx4q8FbHIs7xl+Rf6ldjiRJFrT6aCT2NlY816BkDbjeJUO+gHpV6UVV96rM\nPj4brV6rdjmSJFlAZq6ejadieKauH26OJXMBPhnyBaSx0jC+yXhiMmNYcXGF2uVIkmQBG07FkJVn\nKJEDrnfJkC+Epr5N6RDcgSXnlhCfFa92OZIkmZEQglVHIqnp60r9wHJql1NgMuQLaUyjMRiMBr46\n+ZXapUiSZEanotK4FJtB/+Ylc8D1LhnyhRTgEsDLtV7m9+u/czrhtNrlSJJkJqsOR+Jkq6F7/ZK9\nnaUMeTMYXGcwXg5ezDg6A6Mwql2OJEmFlJ6t4/ezt+jRwB9nu5K9jqMMeTNwtHFkdKPRnE8+z6bw\nTWqXI0lSIf1yMppcvZF+JXjA9S4Z8mbSNbQrdT3r8tXJr8jSZaldjiRJBZSTZ2DVkQjqB5ajlp+b\n2uUUWqHehyiK0hv4GKgBNBVC5G9Pv9jTMLs6OFUAJ8//+ahw/89tHApTZpGwUqyY0GQC/bb0Y/HZ\nxYxuNFrtkiRJeoScPAMXYzM4H5POuZh0zkWncy0xE4NR8EWfemqXZxaF7Ww6DzwHLHyso5y8oHJ7\nyEqCrERIDjd9/qAWsK3zg18A/v13h/KgUacPrY5nHZ6t9CwrLq6gV5VeBLoGqlKHJEn/9e9APx+T\nzt8JpkAH8HCypba/Gx1redMw2J02VT1Vrtg8zLKRt6Iou4F38tuSf+BG3nlZd4L/Tvjf+/j33+88\nJgz3qwYcy9/nBcDr/i8Udi5gxulRCdkJPLP+GcJ8w5jTdo7ZzlscXEy+yLenvyXAJYAwvzAaezfG\n0cZR7bIk6T+0uv8P9LPRDw70ugFu1PZ3o46/G75u9sV+qmTJ38jb1sn04Z6PvU2NRtCmPeLFIAni\nzpk+16bf/zwaO1PY+9aDzp+Ce0ihvgUvRy+G1h3KnJNzOHTrEGF+YYU6X3Hx+/Xf+fjgxzhYO3Ao\n9hArL63E2sqa+p71CfMLI8w3jJoeNdFYadQuVSpj/jfQz0WbWun3C/T2NbypE1ByAt1cHtmSVxTl\nL8DnPl+aJITYeOc5u3lES15RlKHAUICgoKBGERERBa25YPS5kJ0MmQn3f2dw6XcQRugwGRq/BlYF\nH5PONeTSfUN3HKwd+Lnbz1hbFa/X0sehN+r58sSXrLi4gsbejZnVehbOts6cjD/JodhDHL51mEsp\nlwBwtXWlmW8zmvs2J8wvjEAX2V0lmZdWZ+BSbMa9/vMHBXodf7d7LfXSFOgFackXr+4aNaVFwaaR\nEL4TgltB92+gfGiBT7cjYgejd49mUrNJ9K3e14yFFp1UbSrv7n2XI7FH6F+jP2Mbj8XG6r+LNKVo\nUzgSe4RDtw5x8NZB4rNNSzwEOJu6dcL8wmjq0xQ3u5I/U0EqOncD/W6Xy78DvbyTLXX+J9DrBLjh\nV4oC/X5kyBeWEHDqB9g6CYx6aPcRNB1aoFa9EIIh24ZwOfUym3tuLnEBdyXlCqN2jSIxO5EPwj6g\nR+Ue+TpOCMGNjBscumVq5R+NO0q2PhsrxYraHrVp7tecMN8w6nnWw0ZTMlf1k8zvfwP93J1Q/3eg\n1/Z3o24ZCvT7KfKQVxSlJ/AN4AmkAaeFEJ0edVyxDfm70mNg0yi4th2CwqD7PPCo9NinuZp6ld6b\netO3Wl/ea/aeBQq1jD9u/MGHBz7E1c6VOU/NoXaF2gU+l86o41ziOQ7FHuLQrUOcTzqPQRhwsHag\niU8TwnxNLf1Qt9Ay9wtb1uXpjaw9Fsnao1Fcjb+N/l+BXsfflTr+5cpsoN+Pai35x1XsQx5MrfrT\nq+HP98CQC+0+hGbD4TEHFj85/Anrrq5jXbd1VHavbKFizcNgNDDn1By+P/89Db0aMrvNbCo4VDDr\nNW7n3eZo3FEO3TKFfuTtSMA0YH23L7+5b3OzX1cqPgxGwfpTMXz111WiU3NoEFSOFpU8TF0vAeVk\noD+EDHlLyLgFv78NV/+EgKbQYz5UqJLvw9O0aXRd35VaHrVY2GFhsf3Pm56bzri94zh46yAvVHuB\n8U3GF0l3SkxmzL3APxJ3hPRc0yyoqu5VaeHXgjDfMBp6N8Te2t7itUiWJYTgz/NxzN5+lWsJmdTx\nd+PdTtV4okqFYvt7UdzIkLcUIeDsT/DHONBr4amJEPZmvlv1qy6t4rOjn/H1U1/zVNBTFi728V1N\nvcqonaOIz45nUrNJ9KraS5U6DEYDl1Mu3+vaOZVwCp1Rh62VLQ28G9zr2qlevjpWilyRo6QQQrD3\n7yRmbb3CuZh0Kns5807HqnSq5SPD/THJkLe023Hw+xi4shn8G5ta9Z7VHnmYzqij92+9yTPmsaH7\nBmw1tkVQbP5su7mN9w+8j7ONM18+9SX1PIvPrdzZumxOxJ+4F/rX0q4B4G7nTjPfZvfm5/s6+6pc\nqfQgx26mMHPrFY7eSCHA3YG321elRwN/NFYy3AtChnxREALO/wJb3jXdodtmArQY+cilFA7GHGTY\nX8N4u9HbDKo9qIiKfTCD0cC80/NYfG4x9Tzr8WWbL/F0LN63cSdmJ3I49rCpeyf2EEk5SQCEuIYw\nuuFo2gW3U7lC6a7zMenM3naFXVcS8XSxY2TbyrzQJAhba/kOrDBkyBelzATYPAYubQK/BtB9PnjX\nfOghb+14i6NxR9n83GZVBxYz8jIYv3c8+2P206tKLyY2m1is3l3khxCCa2nXOHTrEL+F/8a1tGtM\nf2I6XSp2Ubu0Mi08MZMvtl9l89lY3BxsGNGmEi+HheBgK++ENgcZ8kVNCLiwHra8A9oMaDMeWo6G\nBwxYRmRE0GNjD54JfYapLacWcbEm4WnhjNo1ipjMGN5r+h59qvVRpQ5zytZl8/qO1zmVcIpPWn5C\nt0rd1C6pzIlOzebrHX+z7kQ09jYaBreqyOAnQ3G1l/dCmJMMebVkJZmC/sJ68KkLPb4Fn/vPLf/i\n+Bd8f+F71nRdU6j55wWxI2IHE/dPxMHagS+f+pIGXg2K9PqWlK3LZuTOkRyNO8rkFpPpWaWn2iWV\nCYm3c5m36xqrj0SCAgOaBzOiTSUqONupXVqpJENebRc3wuaxkJMGT74DrcaA9T+7QTLzMum6viuB\nLoH80OWHIpldYBRGvj3zLQvOLKBOhTp82eZLvJ28LX7dopajz2HUzlEcij3ER2Ef8XzV59UuqdRK\nz9axaF843+2/SZ7BSJ/GAbzVtgp+5Yr/3g8lWUFCXo6CmFPN7vD6EajVA3ZPh8VtIfbsP57ibOvM\n6IajOZN4hi03tli8pNt5txm1cxQLziygR+UefN/5+1IZ8AAO1g580+4bWvm3YvKhyay9vFbtkkqd\n7Dw983Zd44nPdzJvVzgdanrz15jWTH+urgz4Ykq25C3l0u+mm6hyUkwt+iffvdeqNwojL25+kaSc\nJDb12GSxNdmvp19n1M5RRN+OZlzTcfSt1rdMzEvOM+QxdvdYdkfvZkLTCfSv0V/tkkq8XL2BNUci\nmbvrGkmZebSv4cWYDtWo6eeqdmllimzJFyc1noE3jkDtXrD3c1jUBm6dAu5sFdh0AgnZCXx3/juL\nXH531G76be5HRl4Gizou4sXqL5aJgAew1djyRZsvaBfUjs+OfsbyC8vVLqnE0huM/HQ8iraz9vDx\npotU9nLmlxEtWPJyExnwJYQMeUtyLA/PLYIX15rWsl/cDnZMAX0uDbwa0KViF5ZdWEZMZozZLmkU\nRhacWcBbO98i2DWYtV3X0sSnidnOX1LYaGyY2XomHYM7Muv4LJacW6J2SSWK0SjYfDaWjl/tZdy6\ns1RwtmXla81YM6Q5jYLd1S5Pegyyu6ao5KSaljA+vQo8a0CPecSV86fb+m48GfAks9vMLvQlsnRZ\nTNw3kZ1RO+kW2o0Pwz4s82u+6I16Ju6fyB83/uCN+m8wvN5wtUsq1oQQ7L6ayKytV7hwK4Oq3s6M\n7ViNjjW9y8w7weKs5G//V5o5uJuWQajVE34bCUva49NiJINqDmT+uUUcjztOY5/H+rf7h4iMCEbt\nHMXNjJuMbzKe/jX6y19KwNrKmumtpmOtWDPv9DwMwsDr9V6XP5v7OHojhZlbL3PsZipB5R358oV6\nPFuvDC5BkJMGB+aAb12o3AHsnNWuqFBkS14N2nRTq/7UD+RUqEL3Cs64OXmytuvaAu2Rujd6LxP2\nTkBjpWFW61k0821mgaJLNoPRwMeHPmbDtQ0MrjOYkQ1GyqC/41x0OrO2XWHP1US8XOwY2a4KfRoH\nls0lCHQ5sLIXRBww/d3aHiq1g5rPQtXO4FBO1fJkS76ksHeD7nOhVg8cfhvFmKhbvOuVzPorP/F8\njRfzfRohBEvPL+Xrk19TrXw1vnrqK/yd/S1YeMmlsdIwucVkrK2sWXJuCXqjnjGNxpTpoL+WcJsv\ntl9ly7k4yjnaMPHp6gwMC8HepowuQWA0wC+DTQH/3GJw9YOLv5mWLrmyGaxsILQ11HgWqncFp5Kx\n54FsyatNm4HY9j6vxP7BTVt7Nj05B9dKbR95WLYum/cPvM/2iO10qdiFyS0m42At5yk/ilEY+fTI\np/x45UdeqvES45qMK3NBH5WSzZwdf/PryWgcbDQMfiKUwU9UxKUsL0EghGk3uJPLofMMaP4/YzdG\nI9w6abrZ8dJvkHoTFCsIbmkK/BrPmF4QioC847UEu3hmBX1PzWRAxm3erfoitP0AbO8/fz4qI4qR\nu0ZyPf06YxqNYWDNgWUuqApDCMHnxz5n5aWV97ZmLAvr00cmZ7N0/3VWH41EURReDgtmRJvKlHdS\nZ3E6ozCyLWIbZxLOMLrRaOw0Ki6FsHOaaarzE2NNu8A9iBAQd87Uur/0GyReNj0e0NTUpVOjG7iH\nWKxM2V1TgtWsN5DnMi6z+vrvPH9iERWv/mnaWza4xT+edyDmAOP2jgPg2/bf0sKvxf1OJz2EoiiM\nazIOaytrll1Yhl7o+aD5B6Uu6I1GwbmYdLZfjGf7xXiuxN/G2kqhT5NA3mpbGV83dd75CSHYGbmT\neWfm8Xfq34BpgHxs47Gq1MORRaaAbzDA1Lh6GEUxDcj61oW2kyDxKlzaaOrW2fa+6cOn7p3A7w6e\nVYvme3hYybIlX3wk5STRbX03GrgEMz/8IqRHw6tbIKg5Qgi+v/A9c07OoVK5Ssx5ag6BLoFql1ws\nRKVk42pvg5vj43U3CCH4+tTXLDm3hJ6Ve/JR2EcFGvguTnL1Bg6GJ7P9Yjw7LsUTn5GLlQJNQsrT\noaY3nWv7EOBumTusH0UIwb6Yfcw9NZdLKZcIdg1mRL0RHIs7xq9//8ryLsuLftG887/CukFQrQv0\n+eGR+0I8VMoNuPy7KfCjj5oeq1DtTuA/Cz51TC8ShSC7a0qB5ReWM+v4LOY9MZMnfxsHikL24O18\ndHwmf978k47BHZnacqrFlkIoKYQQHAxPZsGecPb9nYSiQA0fV8IqedA81IOmFcvj5vDo0BdCMP/M\nfBacWUC30G5MbTm1xAV9WnYeOy8nsP1iPHuvJpKVZ8DRVkPrqp50qOnNU9W8cFepSwZMP+NDsYeY\nd2oeZ5PO4u/sz/B6w3km9BmsrazJ0mXR67deaBQNP3f7uej+b1/fDSufh4DGMGA92JjxnU3GLdPS\nJpd+Mw3kCqOpG6fGs6Y1rvwagtXjv3OUIV8K6Aw6ev7WEwWFXxuMJ35VD0YHVeaqMZtRDUcxqPag\nMt3/bjCaNoNesCecczHpVHC245UWwRgFHApP5kRkKnl6I4oCtfxcCQs1hX6TiuUfurb5gjMLmHd6\nHl0qduHTVp9ibVW8ezIjk7PZdjGO7RfjOR6RisEo8HKxo31NbzrU8CaskkexmCVzLO4Yc0/N5WTC\nSXycfBhadyg9KvfAxsrmP88btHUQ/ar3471m71m+sFunYVlXKBdkerfsYMG7eDMTTbNzLm2C63vA\nqAMXP1P/fc1nISgs3/tFy5AvJfZG7+WNHW/wbKVn2XPjT4w6LTNqDOKJMJX6LIsBrc7AuhPRLN53\nnYjkbCpWcGLok6H0rF0e+z/HQtVOUPs5tDoDp6PSOHw9mUPhyZyKTCPPYMRKgdr+bvdCv3GI+39m\nkyw5t4Q5J+fQMbgjnz352X+CSE1Go+BsTDrbL8bx18UErsTfBqCatwsdanrTvqY3df3dsComNy6d\nTjjN3FNzORJ3BE8HTwbXGczzVZ9/6A5knx39jFWXVrG041Ka+ja1XHHJ4fBdJ7B2gNe2FtnMGMB0\no9XVP01dOuE7QK8FJ0/TlMwa3aBi6wduOgQy5EsNIQQjdozgQMwBKrtVYk5CIkFpsTDiELiUzmWC\nHyQtO4+VhyNYdvAmSZl51Assx4jWoXSo6WO6E3PTKDixzDSl7bnFUOefa8hrdQZORqZy+HoKh8OT\nORWVis4g0Fgp1PZ3o3loecJCPWgcUh5nO+t73WXtgtox88mZ2DzkF87StDoDh8KT2Xanfz3hdi4a\nK4UmIe60r+FNx5o+BHkUr26780nnmXt6LgdiDlDevjyv1X6NPtX65Gt5jRx9Dr039UZn0PHLs7/g\nbGuBO01vx8PSDpB7G17bBhWqmP8a+ZWbCde2mwL/722Ql2m6h6ba06ZunUptweafPzcZ8qVIXFYc\nm8I30a9GP5zSomHhkxDyBPT/udCDNyXBrbQclu6/wZqjkWTnGWhTzZPhrSvRrGL5/++uOrcOfnkN\nmg2HuPMQeQieX2paOuIBcvLuhr6ppX8mOu1e6NcNcKN5qAdah92suzmPNoFtmN16dpHuf5ua9T/9\n638nkp1nwMlWQ+tqnrSvoX7/+oNcTrnMvNPz2B21Gzc7N16t9SovVn/xsfvXTyec5uU/X6Zn5Z58\n3OJj8xapTYfvu0LKdXh5EwQ0Mu/5C0OnhfCdpj78K1tMtdo6Q5UOpsCv0hHsnIs+5BVFmQl0A/KA\ncOBVIUTao46TIV8ARxbBH+9C19nQZLDa1VjMlbjbLNwbzm+nbyGAZ+v5MfTJUGr4/mtZ2+Rw0wuf\ndy14ZTPoc023o8cch97LTTeo5EN2np4TEf8f+mej09EbBXblD2PrvQF/uwaMb/AZzSt6W2wz6ojk\nrHvTHP/Tv17Tm7DQ4tG/fj/XUq8x/8x8tkdsx8XGhYG1BvJSjZcK1Qq/u0Xmt+2/pZV/K/MUqtPC\nqudNDYF+P0Ll9uY5ryXo8+DmPlPgX/odspPuLa+g9FtT5CHfEdgphNArijIDQAgx/lHHyZAvACHu\nrKlxEIbvU/dtppkJITh2M5UFe8LZeTkBBxsNfZsG8lqrivef7qfTwtL2kBYFw/dDuTtTSbUZsPI5\n06DaCyuhWufHriUrV8/xO6G/NWIDifar0GdWQR/7MvX8Pe/N3mkU7F7g4DUaBWei09h+MZ6/LsVz\nNT4TgOo+LrSvYQr2OsWof/1+bqbf5Nsz3/LHjT9wsHbgpZovMbDmQNzs3Ap97lxDLi9seoHbutus\n774eV9tCrltvNMDPL5sGPp9bDHVL0Ob1RoPphenO8grKO5fV665RFKUn8LwQ4pHb8MiQL6CMWPg2\nzDQV67XtDx2gKQmMRsH2S/Es2BPOqcg0yjvZ8kqLEAY0D354l8TmsXBsiWmd/mpd/vk1bTqs6A7x\nF6DvGqhSuBbb2ku/8OnRyXjZ1MYhdQgXorMxCrDVWFE/sBzNK3nQPLQ8DYMeHvpanYGD4Ulsv5jw\nj/71piHl782IKW796/cTdTuKBWcW8Pv137HT2PFi9Rd5pdYruNubd3bKhaQL9N/Sn66hXZnWalrB\nTySEaYe2E99Dp+kQ9rr5iixqRiOKRqNqyG8CfhRCrHzA14cCQwGCgoIaRUREmOW6Zc7FjfDTQNN2\ngm3fV7uaAsnVG9hwKoaFe69zPTGLwPIODH0ilOcbBT66S+T8r7DuVQh7Ezo94Jc/JxWWPwuJV6Df\nWtMAViFsCt/E+wfep6FXQz5r+RUXY3I5dD2Zw9eTOR+Tbgp9aysaBJa719JvEFSOrFwDOy8n8Nd9\n+tfvzl8v51j8+tfvJzYzloVnF7Lx2kY0Vhr6VOvDoNqDqOBguUW65p6ay8KzC/n6qa95Kuipgp1k\n13TY8xm0HA0dJpu3QBVYpE9eUZS/AJ/7fGmSEGLjnedMAhoDz4l8vGrIlnwhrR8BZ9fCq39CUMlZ\nVjhDq2P1kUi+23+DhNu51PJzZVjrSjxd2wdrTT5uDEm5DgueBM9qMOjPh7+TyU6B5d0g+ZppsLri\nk4Wqfcv1LUzcP5F6nvWY334+TjZOAKTn6Dh+M4VD4ckcvpHMhVsZiDuhrzcYMQrwdrW71w0TVskD\nO+vi2b9+PwnZCSw+u5hf/v4FgF5VejGk7hC8HL0sfm2dQUe/Lf1IzE5kQ/cNlLN/zGV+jy0xveur\n/5Jp1ddSMGFBldk1iqK8AgwD2gkhsvNzjAz5QtJmwIKWpmmDw/eDnYvaFT1UQoaWpQdusPpwJLdz\n9bSqXIFhrUNpVblC/m/s0ueapr6l3oRh+8A9+NHHZCXBsmcgLQL6r4OQloX6Prbe3MqEvROoWaEm\nC9ovwMX2vz/39GwdR2+mcOR6Mo62Gtrf6V8vaTewJeUk8d357/jpyk8YjAa6V+7OsLrD8HX2LdI6\nrqRcoe/mvrQPas/M1jPzf+CFDfDzK6b7J15YVbjlCooRNWbXdAa+AFoLIRLze5wMeTOIOAjfPw0N\n7rRSiqHwxEwW7bnO+lMx6I1Gnq7jy7AnK1EnoACDc1vGwdGFpl/YfM6cASAzwXRnY3qM6db1Qr7z\n2RGxg3f2vkN19+os6LDALAONxUmqNpXvL3zP2stryTXk0i20G8PqDVN1naRFZxfxzalvmNl6Jp1D\n8jGYfmOvaZKCXwMYsOGBq7mWRGqE/DXADki+89BhIcQjN9GUIW8mf30M+798/OCzsJORqSzcE862\ni/HYaqzo0ziQwU9UJNjDqWAnvPgb/DQAmr8Onac//vG340wviJkJMHCDaa2SQtgdtZsxu8dQuVxl\nFnVY9PjdCMVQRl4Gyy8sZ+XFleToc+hSsQsj6o0gxC1E7dLQG/UM2DKA6Mxo1ndf//BxgNgzprnw\nbgGm5QocyxddoUVA3gxV1ujzYEk7yIhR/W5YIQS7riSwYM91jt5Iwc3BhpfDghnYIoQKzoVYJzzl\nBixsDR6VYNBWsC7gQGV6DCx7GrJTTUHv37DgNWFaeuLtXW9T0a0iizouorx9yQyTzLxMVl5ayYoL\nK7itu02H4A68Xu91KrtXVru0f7iedp3em3rT0r8lc56ac//ur5TrsLQTaGxNd7O6lb5d0mTIl0UJ\nl2FRa9PAYr+finxwSWcw8tvpWyzae50r8bfxc7PntSdC6dskECe7QvaD6vNMa4wkh8PwvYXfjCEt\nyhT02gx4+TfwrVeo0x2MOcjIXSMJdAlkSccleDh4FK6+IpSty2bN5TV8f+F70nPTaRPYhjfqv0H1\n8tXVLu2Blp1fxuwTs/m01ad0q9Ttn1/MTIClHUGbZmoMeFZTp0gLkyFfVh1eAH+Oh65fQJPXiuSS\nWbl61hw1zZS5la6lmrcLw1qH0q2eHzb5mSmTH3++B4fnm9b5rvmsec6ZGmHqutFlwyu/m+6YLYTD\nsb7KxkMAABhDSURBVId5a8db+Dn7saTjEjwdPc1Tp4Vo9Vp+vPIj353/jhRtCq38W/FG/TeoXaG2\n2qU9ksFo4NWtr3It9Rrru6/H2+nOO1dthmncJfnaneUKCtcdV5zJkC+rjEbTnZ5RR0wzTypY7q12\nUmYuyw/eZMWhCNJzdDSrWJ7hrSvRppqneWeQXN4Ma/tB06Hw9GPMqsiPlOumfltDnmlJBK/CtV6P\nxR3jjR1v4O3ozZKOS/4/fFQghCBLl0VSThLJ2mSSc5L/8fne6L0k5iTSzLcZb9Z/k/pe9VWrtSAi\nMyJ5ftPzNPRuyLftvkUx5JmWK4g4aLo5rkoHtUu0KBnyZVnGLZgfBuVDTf2RZrwbNjNXz45L8Ww+\nG8vuq4noDEY61fRhWOtQGgRZYB3u1AhY+MT/39lrbYG9P5OumVp/wmgK+kJu03Yy/iQj/hqBh4MH\n33X6Dh+n+91aUjBCCLL12f8J7LufJ+UkkZKTcu/zXEPuf85hpVjhbudOVfeqDKk7hCY+TcxWX1Fb\nfWk1049O5+PmH9LrzCbTDYI9F0K9vmqXZnEy5Mu6C+tNc4Nbj4enJhbqVFm5enZcTmDz2VvsupJI\nnt6It6sdT9fx5aXmwVTytMAysGDqh/++CyRdhWF7TC9alpJ41dRHr2hMMzE8KhXqdGcSzzB8+3Dc\n7NxY2mkp/s4PH/jL1pmC+244//vzJK3pzxRtCjn6nP8cr6Dgbu+Oh4MHFewrmP50qICHvQceDnc+\n7E2PlbMrV+J2vHoQozAyZNtgLsSf5NfISPzaToYWb6ldVpGQIS/Br8Pg3M+mwafAx2utZeXq2Xk5\ngc1nY9l1JYFcvREvF1Owd63rS6Mgd8svmrV1EhyaC72XPXTJYLNJuGRq0Vvbm1r05SsW6nTnk84z\ndPtQnG2cGdNoDBl5Gf8N7zst8P9r78zDo6ruPv452feEMCEJiyyGRUgCCgWUyGPBBdlXhdaCiqBW\nH6B9bSu0tVrBt7ZqrX1dHltFRaU+QAQERSCswQWBKoQlBARZs7EkIWHCLOf949whQwSyzcydDOfz\nPPPkzp3lfJ87N9977u+c8/tdybgTwhMumrS7abu2LZHK0BPCE/y+gpW3OL72KcYeXUJGRCvenJgT\ncEXYr4Q2eY1K0PV6lion9kguhF+9x111QRn7p7tOsm5fMVabk6TYcIampzAsszV92vvA2F3kr4KF\n90KfqTD8Jd+0CVC4S6VACItRRl+f1bRXYc+pPUxfM52y6rKL+xLCEy4adGJk4iVm7W7eLSJaXLPG\nXW+2vQ0rfsXiG37KM9aDzOk3h0ndJpmtyidok9coDm9RvdObJsPIV3708vkLDtbnqx57zr4irDYn\nlphwhmakMDQjlZ90SFRVl3xJ2TF4I0stYpm69kcVcbzOiW/hvZEQkaBCN/Ftm/R1Z6xnOFF5AkuE\nMnV/KiXYrNmzXKUNTrsdee8HPLphBjuKdrB4xGKui7vObHVeR5u8poY1T8GWf6h0u92Gcv6Cgw35\nxazYdZJ1e4s5b3NgiQljSHoKwzJa07ejCcbuwmFTF6Wi3fDwpibHxhvN8e3w3miIaqmM3pe1PzV1\nczgXFoyF1EyYvAzCoimsLGTssrGktUhj/l3zA2bc4Uo0G5NPTesh5y/N4ebrWxIfqXs4XsFejfPN\nQdjKTvBM23+ztMBG1QUHLaMNY89MpV/HluYZuztr/gRbXoZxb/2oRqvPOfoNLBijVg/fvxJiPTdL\nRtMECnep9Q2xqSoDqVu6gk8OfsKc3Dk80ecJpvSYYqJI79NsTD6ydReZPPnvBAcJeraNJ6tzEgM7\nW+jZLsFzC2muUaw2Bxv3l7By50kO793GIjGbr0UmqzJfZlhma/p1TKxfWl9fsX81fDgBet8PI/5h\nthrFka9UjzG+rVowFeP9tLqaq3D6kFr5HBRipCu4NJQmpWTm+plsOb6FRSMW0SnBizOyTKbZmHzv\n3n3ka4s+J/dAKZsKStl17CxOCTHhIfTv1JKBXSxkpVnoaIludilazcBqc7Bpfwkrd51k7Z4iKi84\naBEVypD0FKaFfk6n7XNh+MvQ5wGzpV5K2XEVh49rDQ+thdBIsxXVcDgX3h+vZttMWQHRzSdlQUBx\nrgTevlPVB3jw8ysuXCs9X8qYZWNoG9OWBUMXBOzgdbMx+dox+bIqG18cVIafe6CEo6fV1LI2CZHc\n2tlCVmcLA663+GWVerOotjvYvL+UlbtOsmZPEeeq7SREhTKkhwrF9O/UUt0VOZ2wYDQc+0bNtjEr\n3l0bhx3eHQ4nd6r58P5Ys/b7jfDhPdCys8p1E2AZDf2e6gpVD6AkXx3/dn2v+vZVh1fxm42/YcaN\nM5iWOc1HIn1LszX52vxwqlIZfkEJXxw8RYXVjhCQ0SaerDRl+r3bt2hWFXY8QbXdQW5BKSt3KmOv\nqLYTH6mMfWhmKrdc3/Ly4a6y46o2bMvOqjfkDwUUcv4Mm1/0/8LKB3Jg4STVg5y8DCK9sMLX20ip\nUvDmLYGzRyA5XQ1epmSqMQd/vFu2V8MHE9Qd1aSFqvhHPXhi4xPkHMnhP8P+Q9fEwEtSFjAm747d\n4eS7Y2XkGr38HUfO4nBKIkOD6dcpkaw0CwO7JNG5VUxAhnYu2J3kHihh5c5CVu8ppMJqJy4ihLuM\nHvuANEv9xjHylsDiB+G22XDbk94XfjUOrFWhED8ueHIJ+1fDRz9X5jh5KUQ0k0IhJfnqd9+1GE4f\nhKBQFRo761ZfOToJUjKU4buMP/F6CDJx3MbphCVTYXc2jH4dev2s3h89Yz3D6GWjaRXVig+Hfkho\nMy92X5uANPnaVFhtfPX9aXILSthcUMr3pZWAqqM5IM3CwM5JDEizkBTrhXwnXsRqc1BcXk1huZXC\ncivF5Vb2nqxgzZ5Cyq12Yt2N/XoLYSGN+CdcMk39009dbV6mvvKTKg4f0woeymk+VXvyP4OP7oPW\nN8Evsv235OKZH9RvnJcNRbtUicgOt0L6OLhhhAo5WcuhKE/NWDm5Ewq/UymrnTb1HaHRkJKuDD8l\nQ5l/q+7eySFUGynhs9/C1jfhjj/DgJkN/op1R9Yxc/1MHun5CI/1eswLIs3jmjD52hw/e57cghI2\nFZTyxYFSzlSpE7VbSiwDuySRlWahb8dEIkLNCe04nZLTVRcoKrdSVG6lsEwZeVGZMnPXfpdud+Ii\nQrijewrDMlPISktqnLG7c/6sMtjgMHhkM4Q1slJTY3HY4b1RcGIHTN/Q/HJ+71mucgO166tqxtax\nmthnVBSqmqZ5i9XYC0Dbvmo6avfR9SsmY78AJfugcKdh/LvU40KFej0oBJK6XdrrT06HSA9Xxdr0\nN1g3F25+HO6c2+hQ0pzNc/j00Kd8MPQDelialk7an7gmTd4dp1Oy+0Q5mwpKyC0oZfsPZ7jgcBIW\nEkTfDolkdVazdrqnxnlkqb7V5qDQzaxd2xd75GVWiius2ByXHmMhwBITTkpcBMlx4STHRajteOOv\n8TwuMsTzIajDuWowq/f9MOJlz353XaybB5v+CqPfgF7NdBl6XrYKJbQfoIq0mHUnUnUa9i5XvfbD\nuSqbZnIGZIyDHmObnJoBUGGTM4eU8V/s9e+Ec0U170lob4R5etb0+mNTG2fO29+FT2ZA5r3qHGlC\nyKisuoyxy8cSGxrLRyM+Ijy4ed3ZX4lr3uRrU3XBzteHTqt4fkEp+UWqV9IyOowBxgDurZ0tpMZf\nOnXP6ZSUVlZTVHZp+KS2oZdb7T9qMzosmOT4CJJjI0iJdxl2OCnxEbQyzDspNtzc9QCr/whfvAKT\nPoKu9SiM7AkOrleLjHr9DEa/5ps2vcXORfDxdBUG+dlHvpv6WV2hwka7FsPBHHDaVfw8Y7wKx/jq\nzqiiyOjpf1fT6z99sOb1KIth/K5ef8+64/x7V6g6vtcPUnnhPRBLzz2ey6NrH+WB9Af4de9fN/n7\n/AFt8nVQVG41BnBL2VxQSuk5lXc7rVUMnSzRFFdUU1xupbiiGrvz0uMSJKBVrFvPO76mx62eq/2x\nEc1goMdeDf8aDOcKVW3YGC9XM6ooUmGiqESYts73YSJv8O1CWPqoMqWJH3ov147NCgfWKGPf/znY\nz0NcW0gfA+njlYH6w4SD6goozDN6/UbIp3jvpXH+5B41g7upmZB0gzpuh7eoDkBKuqrs5MHz4+kv\nnubjAx/z7pB3m12BlMuhTb4BSCnZV1hBbkEpmwpKKCyzkuxm2ClxNT3vlPgILDHh/pECwFMU7YE3\nb4O0wcqkvGUUToeKwx/bBtPXQ6sbvNOOGexYAMsfh853wb0LPDcw6bCpOfp5S2DfCqguV73jHmNU\nj71dP3Nnv9QX+wUoza8J87h6/e5xfktXKDsKMclqeq+HF51V2ioZu2wsocGhLBqxiMgQP1pw1wi0\nyWsaxpevwudzYMQr0NtLOT82/AU2/C+M/D+46RfeacNMts2HFbOg61CY8C6ENHLBntMJR75Ug6d7\nlkHVKQiPVzNiMsZBh4H+sb6hqTidcPZwjfEX7lJ3lqNehYR2Xmly68mtTF09lftuuI/f9f2dV9rw\nFY0x+QA4azSNpt+jKgSwajZ0yPL8athDm5TJZ05Uc+IDkT4PqNj4p0/Akgdh/Pz6x5OlVDON8rLV\no+IEhERC17tVnD3tdt9MW/QlQUGq2ldiJ+gx2idN9k3ty6Ruk3h/7/sMum5Qsy592Bh0T/5ax7Ua\n1tIFHljlud7iuWIVh4+Ih2nr/We6obf46nVY9aQKqYz999WPY/FeFWPPW6JmrwSFqgLU6eOgy5DA\nP1YmUGWrYsInE3BIB9kjs4kKbSbrM2rRmJ58MwjsabxKfBsY9pKaX53roWpMTgdkT1NVqia8c22Y\nVv9H4Y5nVZ3dpY+oY+DO6UOw6QV47RZ4rb861i3aqzDWbwrU0v2M8dfGsTKBqNAo5mbN5cS5E7y4\n7UWz5fiUJnXbhBDPAqMAJ1AM3C+lPOEJYRofkjEe9q9SoZW0wdCmd9O+b/NL8P0GlTo4OXAWotTJ\ngBlqNknOn1XvfNAfVHw9b7EqSAJq0PTuv0H3UfVbpKTxGDe2upEpPabwzu53GHzdYG5pc4vZknxC\nk8I1Qog4KWW5sT0D6C6lfKSuz+lwjR9y/iy8PkBNaXt4U+OnsR3OVfVS08ep5GP+ML3P12x4HjY8\nV/M8JUNNd0wfCwmBX6LOn6l2VHPPJ/dQaaske1Q2cWFxZktqED4P17gM3iAa8H2AX+MZIhNgzOtw\n6iCs/kPjvqOyFJY8pAbVhv/92jR4gNt+B0NfUMngHjNSPGfN0gbvB4QHhzMvax6l50v569a/mi3H\nJzQ5Ji+EmCeEOAr8HHjqKu+bLoTYJoTYVlJS0tRmNd6g40C4+THY9raaddMQnE7Inq6W2094x38T\nePmKvtNUts+kLmYr0dQi3ZLOg+kPsuzgMjYe3Wi2HK9TZ7hGCLEWuFyhy99LKZe5vW82ECGl/FNd\njepwjR9jr4Y3fwqVJfDLLyHaUr/PbX4Jcp5Rg7g/mepdjRpNE7E5bExcOZHT1tN8PPJjEiI8nGjN\nS3glXCOlvF1KmX6Zx7Jab/0AGNeQxjV+SEg4jPsXWM/C8hlqLndd/PClyhzYYwz0edD7GjWaJhIa\nHMq8rHmctZ7lua3P1f2BZkyTwjVCCPeabaOAfU2To/ELknvA4KcgfyX8d8HV31t5ShUjSbhOrZy9\nVuPwmmZHt8RuPNzzYT479Blrflhjthyv0dSY/F+EEHlCiJ3AnUDDM/xr/JP+j6ksi589Cae/v/x7\nnE41J7yqFO55FyKa10wFjWZqxlS6t+zOs18+y6nzp8yW4xWaOrtmnBG6yZRSjpBSHveUMI3JBAXB\nmDdUEqnsh1XBj9p8+U8oWA13PaeyIWo0zYzQoFDmDZhHpa2SuV/NxYwMAN5Gr3jVXJn4tjDsRTi2\nFXL/fulrR76Gtc+oRT0/ecgcfRqNB0hrkcbjNz7O2iNrWXlopdlyPI42ec3VyZygFjZt/Asc36H2\nVZ024vDtYOQ/dRxe0+yZ3H0yvZJ68dzXz1FcVWy2HI+iTV5TN8NeVPm+s6fDhUpY+ktVAm78fJWA\nTKNp5gQHBTM3ay42h42nv3g6oMI22uQ1dRPZQpXsO1UA/xoE+z9TRZbb3GS2Mo3GY7SPa8+s3rPY\nfHwzSw8sNVuOx9D55DX1o9NtasbNV69Ct+HQ72GzFWk0HmdSt0nkHMnh+W+eZ3vRdqJDo4kKjSI6\nNJrIkEiiQ6PVvpDL7AuNIiwoDOFn4Utt8pr6c/ufVPm+7qN0HF4TkASJIJ4d8CyzN89ma+FWKm2V\nVNmqsMvLzC67DCEihKjQKHVhCIn+0bbrAuG+7bpAuG+7XosMiWzyRUObvKb+hIQHZgk/jcaNNjFt\neO/u9y4+l1Jic9qU4durLhp/la2KSrvadr3m2nZ/XmWv4oz1DJW2Ss7bz1Npq6TaUV0vLQJxyQWg\nMWiT12g0mqsghCAsOIyw4DBa0MIj32l32msuAq4Lg9sFw3UxqH3xWMGKBrelTV6j0Wh8TEhQCHFh\ncQ3OZ/8CLzS4LT27RqPRaAIYbfIajUYTwGiT12g0mgBGm7xGo9EEMNrkNRqNJoDRJq/RaDQBjDZ5\njUajCWC0yWs0Gk0AI8xIqSmEqADyfd7w1bEApWaLqIU/agL/1KU11Q+tqf74o66uUsrYhnzArBWv\n+VLKPia1fVmEENu0pvrhj7q0pvqhNdUff9QlhNjW0M/ocI1Go9EEMNrkNRqNJoAxy+TfNKndq6E1\n1R9/1KU11Q+tqf74o64GazJl4FWj0Wg0vkGHazQajSaA8anJCyGGCCHyhRAHhBBP+rLtWjreFkIU\nCyHy3PYlCiHWCCEKjL+eqQ5Qf03thBDrhRB7hBC7hRAzzdYlhIgQQmwVQnxnaHrGbE1u2oKFEP8V\nQqzwB01CiMNCiF1CiG9dMyDM1mRoSBBCLBZC7BNC7BVC3GzyOdXVOEauR7kQYpbZx0oI8SvjHM8T\nQiw0zn2zNc009OwWQswy9jVYk89MXggRDLwK3A10ByYJIbr7qv1avAMMqbXvSSBHStkZyDGe+xI7\n8D9Syu5Af+Ax4/iYqasaGCSl7An0AoYIIfqbrMnFTGCv23N/0PRTKWUvt2l3/qDpH8AqKWU3oCfq\nmJmmS0qZbxyjXkBvoAr42ExNQog2wAygj5QyHQgGJpqsKR2YBvRF/W7DhRBpjdIkpfTJA7gZ+Nzt\n+Wxgtq/av4yeDkCe2/N8INXYTkXN5TdFm6FhGXCHv+gCooAdQD+zNQFtjRN8ELDCH34/4DBgqbXP\nbE3xwCGMsTd/0eWm405gi9magDbAUSARtXZohaHNTE0TgLfcnv8R+G1jNPkyXOM6kC6OGfv8hWQp\n5UljuxBINkuIEKIDcCPwNSbrMsIi3wLFwBoppemagJdRJ7zTbZ/ZmiSwVgixXQgx3U80dQRKgPlG\naOvfQohoP9DlYiKw0Ng2TZOU8jjwAnAEOAmUSSlXm6kJyANuFUK0FEJEAUOBdo3RpAdeL4NUl0lT\nph0JIWKAJcAsKWW52bqklA6pbq3bAn2N20jTNAkhhgPFUsrtV3qPSb9flnGc7kaF2gb6gaYQ4Cbg\ndSnljUAltW7vzTrXhRBhwEhgUe3XTDinWgCjUBfF1kC0EOI+MzVJKfcCzwOrgVXAt4CjMZp8afLH\nUVciF22Nff5CkRAiFcD4W+xrAUKIUJTBfyClzPYXXQBSyrPAetRYhpmaBgAjhRCHgf8Ag4QQ75us\nydUbREpZjIox9zVbE+pu+Zhx9wWwGGX6ZusCdTHcIaUsMp6bqel24JCUskRKaQOygVtM1oSU8i0p\nZW8p5UDgDLC/MZp8afLfAJ2FEB2Nq/hEYLkP26+L5cAUY3sKKibuM4QQAngL2CulfMkfdAkhkoQQ\nCcZ2JGqMYJ+ZmqSUs6WUbaWUHVDn0Dop5X1mahJCRAshYl3bqHhunpmaAKSUhcBRIURXY9dgYI/Z\nugwmUROqAXM1HQH6CyGijP/DwagBarM9oZXx9zpgLPBhozT5aiDBGCgYiroaHQR+78u2a+lYiIq9\n2VC9nalAS9RgXgGwFkj0saYs1K3XTtSt2bfG8TJNF5AJ/NfQlAc8Zew39Vi56buNmoFXM49TJ+A7\n47HbdW77w3FCzYraZvyGS4EWZusCooFTQLzbPrM1PYPqwOQBC4BwP9C0GXVR/g4Y3NjjpFe8ajQa\nTQCjB141Go0mgNEmr9FoNAGMNnmNRqMJYLTJazQaTQCjTV6j0WgCGG3yGo1GE8Bok9doNJoARpu8\nRqPRBDD/D99rrSfULieTAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1daef756b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = DataFrame(np.random.randn(10, 4).cumsum(0),\n",
    "               columns=['A', 'B', 'C', 'D'],\n",
    "               index=np.arange(0, 100, 10))\n",
    "df.plot() # 每一列是一条曲线"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Series.plot方法的参数\n",
    "# label\n",
    "# ax：       绘制的subplot对象\n",
    "# style\n",
    "# alpha\n",
    "# kind：     图像类型：line/bar/barh/kde\n",
    "# logy\n",
    "# use_index：将对象的索引用作刻度标签\n",
    "# rot：      旋转刻度标签\n",
    "# xticks\n",
    "# yticks\n",
    "# xlim\n",
    "# ylim\n",
    "# grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 专用于DataFrame.plot方法的参数\n",
    "# subplots：    将各个DataFrame列绘制到单独的subplot中\n",
    "# sharex/y\n",
    "# figsize\n",
    "# title\n",
    "# legend\n",
    "# sort_columns：以字母顺序绘制各列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 柱状图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1daefba57b8>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Rz1E97SN5Xm5bfnmi49peCiwFaLVazlfmiIj+GMTJ3YcHcMyIiChlVE9ERMP0fa6e/Q4o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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1daefb18d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(2, 1)\n",
    "data = Series(np.random.rand(16), index=list('abcdefghijklmnop'))\n",
    "data.plot(kind='bar', ax=axes[0], color='k', alpha=0.7)\n",
    "data.plot(kind='barh', ax=axes[1], color='k', alpha=0.7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1daefcf8d30>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1daec6e2438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = DataFrame(np.random.rand(6, 4),\n",
    "               index=['one', 'two', 'three', 'four', 'five', 'six'],\n",
    "               columns=pd.Index(['A', 'B', 'C', 'D'], name='Genus'))\n",
    "df.plot(kind='bar') # 默认并列柱状图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1daefe0e518>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1daec7b2208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(kind='barh', stacked=True, alpha=0.5) #  stacked=True表示堆叠柱状图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>size</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Fri</th>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sat</th>\n",
       "      <td>2</td>\n",
       "      <td>53</td>\n",
       "      <td>18</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sun</th>\n",
       "      <td>0</td>\n",
       "      <td>39</td>\n",
       "      <td>15</td>\n",
       "      <td>18</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thur</th>\n",
       "      <td>1</td>\n",
       "      <td>48</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "size  1   2   3   4  5  6\n",
       "day                      \n",
       "Fri   1  16   1   1  0  0\n",
       "Sat   2  53  18  13  1  0\n",
       "Sun   0  39  15  18  3  1\n",
       "Thur  1  48   4   5  1  3"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips = pd.read_csv('../data/tips.csv')\n",
    "# 书上的写法有问题，tips.size返回记录条数，而不是取'size'列。\n",
    "party_counts = pd.crosstab(tips['day'], tips['size']) # 根据day和size做group然后计数\n",
    "party_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>size</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Fri</th>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sat</th>\n",
       "      <td>53</td>\n",
       "      <td>18</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sun</th>\n",
       "      <td>39</td>\n",
       "      <td>15</td>\n",
       "      <td>18</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thur</th>\n",
       "      <td>48</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "size   2   3   4  5\n",
       "day                \n",
       "Fri   16   1   1  0\n",
       "Sat   53  18  13  1\n",
       "Sun   39  15  18  3\n",
       "Thur  48   4   5  1"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "party_counts = party_counts.iloc[:, 1:5] # 取人数2-5的聚会\n",
    "party_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>size</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Fri</th>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sat</th>\n",
       "      <td>0.623529</td>\n",
       "      <td>0.211765</td>\n",
       "      <td>0.152941</td>\n",
       "      <td>0.011765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sun</th>\n",
       "      <td>0.520000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.240000</td>\n",
       "      <td>0.040000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thur</th>\n",
       "      <td>0.827586</td>\n",
       "      <td>0.068966</td>\n",
       "      <td>0.086207</td>\n",
       "      <td>0.017241</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "size         2         3         4         5\n",
       "day                                         \n",
       "Fri   0.888889  0.055556  0.055556  0.000000\n",
       "Sat   0.623529  0.211765  0.152941  0.011765\n",
       "Sun   0.520000  0.200000  0.240000  0.040000\n",
       "Thur  0.827586  0.068966  0.086207  0.017241"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 先对每一行求和\n",
    "# 然后沿着行的方向每行各个元素除对应行的和\n",
    "party_pcts = party_counts.div(party_counts.sum(1).astype(float), axis=0)\n",
    "party_pcts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1daefb302b0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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hZs9dkgpkuEtSgYbvsEyDf6rNHMCfau0wPUrS8GDPXZIKZLhLUoEMd0kq0PAdc5eGkS1T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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1daeff23da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "party_pcts.plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 直方图和密度图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1daeff230f0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ul3S0pNsi4snSkgEA2tLVnaIRca+ke0vKMoiG8VARmaszjLnJXI2+ZO74pCgAYLBw6z8A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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1daefffa780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tips['tip_pct'] = tips['tip'] / tips['total_bill']\n",
    "tips['tip_pct'].hist(bins=50) # 消费金额比例的分布,貌似15%的比例最高。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1daf0103f98>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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wYZvIz2sKXRoubDAERV06IiLr2Gpa+EGO0vklcJmZbTezCuC3gO8FeDwREVlG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\n+CIiJUKBLyJSIhT4IiIlQoEvIlIi/j+YxWRC8AF/JAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1daef588c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tips['tip_pct'].plot(kind='kde') # 概率密度曲线"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1daf02156d8>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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I8niqXLkyEyZMYNu2bfTr1y8hb3RV1qhgCABpaWlMmzaNZ599tsxfD1BQ9erV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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1daf1afa828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "comp1 = np.random.normal(0, 1, size=200) # N(0, 1)\n",
    "comp2 = np.random.normal(10, 2, size=200) # N(10, 4)\n",
    "values = Series(np.concatenate([comp1, comp2])) # 连城400个元素的Series\n",
    "values.hist(bins=100, alpha=0.3, color='k', normed=True) # 归一化后直方图\n",
    "values.plot(kind='kde', style='k--') # 画在同一个plot上"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 散布图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cpi</th>\n",
       "      <th>m1</th>\n",
       "      <th>tbilrate</th>\n",
       "      <th>unemp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.005849</td>\n",
       "      <td>0.014215</td>\n",
       "      <td>0.088193</td>\n",
       "      <td>-0.128617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.006838</td>\n",
       "      <td>-0.008505</td>\n",
       "      <td>0.215321</td>\n",
       "      <td>0.038466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.000681</td>\n",
       "      <td>-0.003565</td>\n",
       "      <td>0.125317</td>\n",
       "      <td>0.055060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.005772</td>\n",
       "      <td>-0.002861</td>\n",
       "      <td>-0.212805</td>\n",
       "      <td>-0.074108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.000338</td>\n",
       "      <td>0.004289</td>\n",
       "      <td>-0.266946</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        cpi        m1  tbilrate     unemp\n",
       "1  0.005849  0.014215  0.088193 -0.128617\n",
       "2  0.006838 -0.008505  0.215321  0.038466\n",
       "3  0.000681 -0.003565  0.125317  0.055060\n",
       "4  0.005772 -0.002861 -0.212805 -0.074108\n",
       "5  0.000338  0.004289 -0.266946  0.000000"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "macro = pd.read_csv('../data/macrodata.csv')\n",
    "data = macro[['cpi', 'm1', 'tbilrate', 'unemp']]\n",
    "trans_data = np.log(data).diff().dropna() # 对数运算后求差值\n",
    "trans_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1daf1db5b70>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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1qpMgIhk/Kb/dfPpFaR1HK7LZ9C784q37JzEaHxtlzVmP1kW8e8PN++sQpCRN\nvptD2SeTKuepaBKkEYl9EZW/p0aTRDWbCMqTKg03P1EsUFUuSFXvIptd4MvchdbbZ1Hv5uz6X/uU\nkE/fTl+G8cWjrPrcjbOmNt09PcOqf7wRYE7P6QjmzD3R6MkkO19Tu6cRs+fkbnSeivobZMda9W6/\nTL8G932wThqYfhTDqHYyneyCsmFz/RbEVXrQ1tv2p6/7UeUeuK322p6e2cuajbd2rNf36IiIYFaQ\nyMzsi4Y9p2vVu5jmzxcUB5h6yvQ3qNLTuUy/Bvd9sE5yoOhjVYdOqHIXWW/bRffyZXs2V7V7eqbl\nealrLTnwgIaz3E1V6Dmdv5hu2DzFKWuv5W3rtzQNaEXnomw/lLLnsky/Bvd9sE5y0VMfq1p8UDSn\nQu2Fr8xscEXpy+6z2x6YTkZyLeqPkfWEbhYs8hfTsvNPZIrOU20/hKJ6mLJ3+2X6Nbjvg3WSA0Uf\nK3Phz2s2p0LVC19t+rL77IWl42M89MiewuV7I3jNc46qW3+yeHQR0zP7Zk29WlQZXCQ7T0V1SvkO\neUXfw0OP7GHD5qlSF/Myc2/UrpM9HTlwWFUOFH2s7FzTmWZ3kVXrA2pbJzXbZ9GFNWvtNF8EnPa0\nQ/l0gyHCJ8bHCnte56dILRqSvJGJXICp15hg8q775vScvuhlJ8zq7Q1w/0MzdZu5VmnMULRuPzeX\ndX+P/ufmsX2uk/+Jjln9pcJ6iFoC7lz74o5sP2u2Whv08i2H6hkfGwWYM+zG5F338enrfjQr7djo\nCI8dXbS/WWy9/Zc5byde+JXC4qt68s1pi55Cao8zS1MUXLMmrFU6FjZbt1+byw5i58lB5uaxC1S7\nc03nValPaKV1TNH2xxePFo5RtP767XVbKkFS71AvWF28aWvdVkePOWARY6Mjc4LROc85qvQ5rBIk\nsv1mzWmL6o6KWkg1q4Nq1Jih9niarduvzWWrHKP1jgPFEClbn9Bq65hVpx83px8DwC9++WjZe+1/\n/uVHH8IFV26pWzRVFKyKLm4PTM/w4Vee2Nb0oq3I8lMlEE/tnt4/gGKt7LirXNybrVu1vqtb+jWA\n2WxuHttBWWXhMau/xClrry3s79ArK5dNcNHLTmAivThkLYHGx0Y5ePEoIimKaPWxf+WyCZYcOPfe\nI+vHUCu7OEckd/55jYLVQWmRVL3PVy6b4Furn8uda1/Mt1Y/t9RxvHvDzVywfkvLrbeyi23V4djr\nBYn8cVfznI/DAAANj0lEQVTpC9Fs3X5tLuv+HoPBTxQd0s+VhXmdLMqqp6gvQ/4OccPmqVnDbEBS\nPJOV5ecr0evV0RTND9TKvEMbNk+1NE92Jn+xrVe8Vib4FA3lUaUxQ7N1+7W5bNUGG9YbDhQd4rLW\nRLMijkZNdLMgkVXk1lYsZ8G3qOhsd52K7GYu/OKtldPk59yo138h/75MM9t9EXXrYqpc3Mv2rei3\n32K/BjCbzYGiQ1zWmqh3h5g1X4XmTXR37p5uGEymZ/YWNrdtpbiiXiupIq20ECpTLxQkAaXeBbLK\nxb0fA0EZg5rvYeJA0SH9WlnYTVkxUb2hQT5/wxTLjz6kaeBcOj7WNJgUtejOgtF8aLU4JLsAvm39\nlobr9WtRpRm4Mrtj+rWysFs2bJ5i1eduLCxmyYrhGgXO7Hy1+hT29dt3VU4zXlAxDo9W9rdTwZ8p\nU31SZWBAs25yoOiQfIuidlsPDaILv3hrYX+IzM7d04Utg8bHRvefr1afwloJMGvOOp7RRfUv43sj\nGF0kHnpkDxes31K6JVtt67c1G28t3dFx2IoqbTC0VfQk6RBgPfBU4IfAKyLi/jrrnQH8Fcm82B+P\niLXp52uAtwLZreA7I+LqdvLUS8Nc1lqmrH/p+FipystVpx/HBeu3lL645rdfVbMhSGb2xf5ja3V+\njiqGqajSBke7dRSrga9FxFpJq9P3f5ZfQdIIcAnwAmAHcL2kjRFxW7rKhyPig23mw/pcbVPNZuNH\nNSvTX6S5M9PlBz+s7QFeO9ZSvZFWm+0TmrdkqzKeVr2hPYalqNIGS7tFTyuAT6avPwmsrLPOScC2\niLgjIh4BrkjT2QLSqKy/lWK4iSZ31geNjdYt5qs3IdPl1/2o6eRPVeoGOjE/x9joCOc856ihLaq0\nwdLuE8VhEXF3+vrHwGF11pkA8lOL7QBOzr3/Q0mvAyaBP65XdAUg6VzgXICjjjqqzWxbp6056/g5\no66OLhIX/94zW7r4NWtWuvuhGTa/54VzPi9zRz89s5e3X5k8PWR5q1I3UG9+j+xpZXzxaN1iuIMX\nj7L4wAPmPNXk02fBysHC+k3TQCHpGuApdRa9K/8mIkJS1WLljwHvI3kCfx/wIeBN9VaMiHXAOkhG\nj624H5tnneo4lb9wHjQ2ysN79lJvxO+q40DV2hew6nM37s972V7Ujeb3mNo9zegiMTqiWRX7Y6Mj\nvPelxzcd8dVNZK1fNQ0UEfH8omWSfiLp8Ii4W9LhwD11VpsCjsy9PyL9jIj4SW5bfwf8c9mMW/9p\ntzK/9sK5e3qG0UViZBFzLrxFZflVBuab2Rv76xuKhpJ4+bMnCus36j29zOwLxsdGWfKYuU8Ptdyb\n3wZFu0VPG4HXA2vTf/+pzjrXA8dKOoYkQLwKeDVAFmTS9X4HuKXN/NgAa/fCC9Vn3MueQFp5Imo0\niu2W984tFiub3k1krd+0GyjWAldKejNwF/AKAElLSZrBnhkReySdD2wiaR57WURkA+x8QNKJJEVP\nPwT+e5v5sQFW9sLbaErPehf8+x58mOmZfXW3nS/CqvpE1G5vfPfmt0HRVqCIiHuB59X5fCdwZu79\n1cCc/hER8dp29m+DrbYi+KCx0boTB9VWHjcr1683V3S96U1HR9S0OWqjGQbbHfnUI6faoPBYT9YT\ndSuCR8ToIs26oNdeOFsp188+z89RnU2r2ugJollQarcC3yOn2qDwnNnWFbV35g89sqdSM9JMo3m5\nq87x3Uy/zjNt1irPmW19q8qwFkX9IzLdLNcvqjOZSodC952/DQsPCmjzrsqwFs0u+N0cpbdRXt62\nfgvv3nBzx/dp1o/8RGEtaVTJW6vKsBbNLvjdLNdv1tT28ut+xPKjD9m/7yrnxGyQOFBYZVV7FBcV\nF1XpH5HXrVF6ywwWmFWiu5e1LWQuerLKGrU8qqeouGjNWcfzrdXP5c61L+Zbq5/blxfUZnnKnpaq\nnhOzQeInCqusao/iQW8GenDBQH/waD2Ge1nbQuZAYZW10vJokCd1eu9Lj+ftV26ZMzhhvsOee1nb\nQuaiJ6ts2OYHX7lsgr98xYmz5tw4ePEoF//uM2f10h6mc2LDxU8UVtmgFyW1osysfDBc58SGh3tm\nm5kNiVZ7ZrvoyczMGnLRkw0Ud2oz6z4HChsY7tRm1hsuerKB4U5tZr3hQGEDw53azHqjrUAh6RBJ\nX5X0g/TfgwvWu0zSPZJuaSW9GRR3XnOnNrP51e4TxWrgaxFxLPC19H09nwDOaCO9Wd1ObQJOe9qh\nbW03m4P7mNVf4pS117Jh81Rb2zNbaNoNFCuAT6avPwmsrLdSRHwTuK/V9GaQVFi//NkTKPdZAJ+/\nYarli3tWQT61e5rg0QpyBwuzR7UbKA6LiLvT1z8GDpuv9JLOlTQpaXLXrl0tZNUWgq/fvmvOVKjt\nVGi7gtysuabNYyVdAzylzqJ35d9EREhquZt3s/QRsQ5YB0nP7Fb3Y4Ot0xXariA3a65poIiI5xct\nk/QTSYdHxN2SDgfuqbj/dtPbkOn0KK0e9dWsuXaLnjYCr09fvx74py6ntyHT6VFaPeqrWXPtBoq1\nwAsk/QB4fvoeSUslXZ2tJOmzwHeA4yTtkPTmRunNiqxcNsFFLzuBifExBEyMj3HRy05ouWd2p7dn\nthB59FgzsyHh0WPNzGxeOFCYmVlDDhRmZtaQA4WZmTXkQGFmZg05UJiZWUMOFGZm1pCnQrWh5Lm3\nzcpzoLCh47m3zapx0ZMNHQ8tblaNA4UNHQ8tblaNA4UNHc+9bVaNA4UNHQ8tblaNK7Nt6GQV1m71\nZFaOA4UNpZXLJhwYzEpy0ZOZmTXkQGFmZg05UJiZWUNtBQpJh0j6qqQfpP8eXLDeZZLukXRLzedr\nJE1J2pL+ndlOfszMrPPafaJYDXwtIo4Fvpa+r+cTwBkFyz4cESemf1e3mR8zM+uwdgPFCuCT6etP\nAivrrRQR3wTua3NfZmbWA+0GisMi4u709Y+Bw1rYxh9KuiktnqpbdAUg6VxJk5Imd+3a1VJmzcys\nuqaBQtI1km6p87civ15EBBAV9/8x4FeAE4G7gQ8VrRgR6yJieUQsP/TQQyvuxszMWtW0w11EPL9o\nmaSfSDo8Iu6WdDhwT5WdR8RPctv6O+Cfq6Q3M7P5127R00bg9enr1wP/VCVxGlwyvwPcUrSumZn1\nRruBYi3wAkk/AJ6fvkfSUkn7WzBJ+izwHeA4STskvTld9AFJN0u6CTgNuKDN/JiZWYe1NdZTRNwL\nPK/O5zuBM3Pvzy5I/9p29m9mZvNPSR30YJG0C7ir1/nokCcBP+11JuaRj2+w+fgGX/4Yj46Iyq2B\nBjJQLCSSJiNiea/zMV98fIPNxzf4OnGMHuvJzMwacqAwM7OGHCh6b12vMzDPfHyDzcc3+No+RtdR\nmJlZQ36iMDOzhhwozMysIQeKLqgwwdMZkrZK2iZpde7ziyXdno6y+wVJ493LfbGi/OaWS9Jfp8tv\nkvSssmn7QavHJ+lISV+XdJukWyX9j+7nvrl2vr90+YikzZL6coy2Nn+f45I+l/6/+56k3+hu7ptr\n8/guSH+bt0j6rKTHNtxZRPhvnv+ADwCr09ergb+os84I8B8ko+keCNwIPD1d9kLggPT1X9RL34Nj\nKsxvbp0zgS8DAp4DfLds2l7/tXl8hwPPSl8/Hvj+Qjq+3PK3A58B/rnXx9Pp4yOZX+ct6esDgfFe\nH1MHf58TwJ3AWPr+SuANjfbnJ4ruKDPB00nAtoi4IyIeAa5I0xERX4mIPel61wFHzHN+yyjMb84K\n4FORuA4YTweCLJO211o+voi4OyL+HSAifg58j+Q/Zz9p5/tD0hHAi4GPdzPTFbR8fJIOAv4b8PcA\nEfFIROzuZuZLaOv7Ixm+aUzSAcBiYGejnTlQdEeZCZ4mgO259zuof3F5E8ldQq+VyW/ROmWPtZfa\nOb79JD0VWAZ8t+M5bE+7x/cR4E+BffOVwTa1c3zHALuA/5MWrX1c0pL5zGwLWj6+iJgCPgj8iGQe\noAci4iuNduZA0SGa3wmesn28C9gDfLoDWbZ5JulxwOeBt0XEz3qdn06R9BLgnoi4odd5mScHAM8C\nPhYRy4AHSYqMF4S0jnQFSUBcCiyR9JpGadoaPdYeFe1P8DQFHJl7f0T6WbaNNwAvAZ6XBptea5jf\nJuuMlkjba+0cH5JGSYLEpyPiqnnMZ6vaOb6XA2dJOhN4LPAESZdHRMOLTZe1c3wB7IiI7Cnwc/Rf\noGjn+J4P3BkRuwAkXQX8JnB54d56XSkzDH/AxcyuzP5AnXUOAO4gifJZ5dTx6bIzgNuAQ3t9LGXy\nm1vnxcyuTPu3sml7/dfm8Qn4FPCRXh/HfBxfzTq/TX9WZrd1fMD/BY5LX68BLu71MXXw93kycCtJ\n3YRI6k3/sOH+en3Aw/AHPBH4GvAD4BrgkPTzpcDVufXOJGkh8x/Au3KfbyMpa9yS/l3a62Mqyi9w\nHnBe+lrAJenym4HlzY61n/5aPT7gVJK70pty39mZvT6eTn5/uW30ZaDowO/zRGAy/Q43AAf3+ng6\nfHwXAreTzCr6D8BjGu3LQ3iYmVlDrsw2M7OGHCjMzKwhBwozM2vIgcLMzBpyoDAzs4YcKMzMrCEH\nCjMza+j/A8y6BtHKQQ9RAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1daf1b04470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(trans_data['m1'], trans_data['unemp'])\n",
    "plt.title('Changes in log %s vs. log %s' % ('m1', 'unemp'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel\\__main__.py:1: FutureWarning: pandas.scatter_matrix is deprecated. Use pandas.plotting.scatter_matrix instead\n",
      "  if __name__ == '__main__':\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000001DAF1DE6588>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001DAF1E70860>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001DAF1ED8D68>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001DAF1F314A8>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x000001DAF1F988D0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001DAF1F98908>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001DAF2049400>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001DAF209DBE0>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x000001DAF2104EB8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001DAF215FA20>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001DAF21C5F28>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001DAF21D56A0>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x000001DAF2286D68>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001DAF22F60B8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001DAF23469B0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001DAF23AFCC0>]], dtype=object)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Zazdu3MBoNOLz+ZDJZMzOzkoxgWg0+lBdJKfTSTAYpLq6GoPBQEZGBvv27ZM6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\nhs7Z58880ZsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1daf1b04c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.scatter_matrix(trans_data, diagonal='kde', color='k', alpha=0.3) # 任意2个指标的散点图，主对角线画密度函数。"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
